Skip to main content

Advertisement

SpringerLink
Log in

Secure Coronas Based Zone Clustering and Routing Model for Distributed Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Distributed Wireless Sensor Networks (DWSNs) comprised of set of sensor nodes that are geographically distributed in harsh environments. However, a centralized WSN does not consider the network scalability and also leads to high energy consumption. Due to these open issues, distributed computing approach is considered since it resolves scalability issue and several paths are established for data transmission. To achieve higher energy efficiency, scalability and security, in this paper we propose a distributed protocol called Secure Coronas-Based Zone Clustering and Routing (SC-ZCR). The proposed SC-ZCR aim at addressing the number of issues in DWSN and it support for long-term deployment. In SC-ZCR, we will pursue several processes including Zone Clustering, Energy Efficient Routing and Data Encryption and Security Verification. Zone clustering is carried out using Adaptive Neuro-Fuzzy System, where we consider four parameters: node angle, distance between sensor node to the sink node, node residual energy and belief value. Belief value of each sensor node is computed using Principal Component Analysis. Then energy efficient routing is established by Q-Hop Routing Protocol, which finds optimum and shortest path using Whale Optimization Algorithm. For data packets encryption, RC6 is used and then security level of data packets are verified using potential weight factor \(\delta\), which is computed using key size \(K\), block size \(B\), and number of rounds \(R\). Experiments conducted using NS3.26 simulator and the simulation result show that the proposed SC-ZCR outperforms in terms of Coverage Ratio, Residual Energy, Network Lifetime, Delay, Packet Drop Rate, and Security Strength.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Salim, A., & Osamy, W. (2015). Distributed multi-chain compressive sensing based routing algorithm for wireless sensor networks. Wireless Networks,21(4), 1379–1390.

    Google Scholar 

  2. Zhao, Z., Kaida, X., Hui, G., & Liqin, H. (2018). An energy efficient clustering routing protocol for wireless sensor networks based on AGNES with balanced energy consumption optimization. Sensors,18(11), 1–27.

    Google Scholar 

  3. Zaman, N., Tang Jung, L., & Yasin, M. M. (2016). Enhancing energy efficiency of wireless sensor network through the design of energy efficient routing protocol. Journal of Sensors,2016, 1–16.

    Google Scholar 

  4. Arapoglu, O., Akram, V. K., & Dagdeviren, O. (2019). An energy-efficient, self-stabilizing and distributed algorithm for maximal independent set construction in wireless sensor networks. Computer Standards and Interfaces,62, 32–42.

    Google Scholar 

  5. Al-Baz, A., & El-Sayed, A. (2017). A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks. International Journal of Communication Systems,31(1), 1–13.

    Google Scholar 

  6. Kumar, A., Ilango, P., & Dinesh, G. H. (2016). A modified LEACH protocol for increasing lifetime of the wireless sensor network. Cybernetics and Information Technologies,16(3), 154–164.

    Google Scholar 

  7. Behera, T. M., Mohapatra, S. K., Samal, U. C., Khan, M. S., Daneshmand, M., Gandomi, A. H. (2019). Residual energy based cluster-head selection in WSNs for IoT application (pp. 1–8).

    Google Scholar 

  8. Kang, J., Sohn, I., & Lee, S. H. (2019). Enhanced message-passing based LEACH protocol for wireless sensor networks. Sensors,19(1), 1–17.

    Google Scholar 

  9. Huang, J., Zhao, Z., Yuan, Y., & Hong, Y. (2017). Multi-factor and distributed clustering routing protocol in wireless sensor networks. Wireless Personal Communications,95(3), 2127–2142.

    Google Scholar 

  10. Nokhanji, N., Hanapi, Z. M., Subramaniam, S., & Mohammed, M. A. (2015). An energy aware distributed clustering algorithm using fuzzy logic for wireless sensor networks with non-uniform node distribution. Wireless Personal Communications,84(1), 395–419.

    Google Scholar 

  11. Dohare, U., Lobiyal, D. K., & Kumar, S. (2014). Energy balanced model for lifetime maximization in randomly distributed wireless sensor networks. Wireless Personal Communications,78(1), 407–428.

    Google Scholar 

  12. Chen, D.-R., Chen, L.-C., Chen, M.-Y., & Hsu, M.-Y. (2019). A coverage-aware and energy-efficient protocol for the distributed wireless sensor networks. Computer Communications,137, 15–31.

    Google Scholar 

  13. Lakshmi, N. V. S. S. R., Babu, S., & Bhalaji, N. (2019). Analysis of clustered QoS routing protocol for distributed wireless sensor network. Computers and Electrical Engineering,64, 173–181.

    Google Scholar 

  14. Liu, Y., & Wu, Y. (2019). A novel sub-regional key distribution scheme for distributed wireless sensor networks. International Journal of Wireless Information Networks,26, 1–6.

    Google Scholar 

  15. Das, A. K. (2015). A secure and efficient user anonymity preserving three-factor authentication protocol for large-scale distributed wireless sensor networks. Wireless Personal Communications,82(3), 1344–1404.

    Google Scholar 

  16. Nisha, U. B., Maheswari, N. U., Venkatesh, R., & Abdullah, R. Y. (2016). Fuzzy-based flat anomaly diagnosis and relief measures in distributed wireless sensor networks. International Journal of Fuzzy Systems,19(5), 1–18.

    Google Scholar 

  17. Kim, H., & Han, S.-w. (2015). An efficient sensor deployment scheme for large-scale wireless sensor networks. IEEE Communications Letters,19(1), 98–101.

    Google Scholar 

  18. Pachlor, R., & Shrimankar, D. (2017). VCH-ECCR: A centralized routing protocol for wireless sensor networks. Journal of Sensors,2017, 1–10.

    Google Scholar 

  19. Lin, Y.-H., Chou, Z.-T., Chun-Wei, Yu., & Jan, R.-H. (2015). Optimal and maximized configurable power saving protocols for corona-based wireless sensor networks. IEEE Transactions on Mobile Computing,14(2), 2544–2559.

    Google Scholar 

  20. Khabiri, M., & Ghaffari, A. (2018). Energy-aware clustering-based routing in wireless sensor networks using cuckoo optimization algorithm. Wireless Personal Communications,98(3), 2473–2495.

    Google Scholar 

  21. Ayati, M., Ghayyoumi, M. H., & Keshavarz-Mohammadiyan, A. (2018). A fuzzy three-level clustering method for lifetime improvement of wireless sensor networks. Annals of Telecommunications,73(7–8), 535–546.

    Google Scholar 

  22. Khoulalene, N., Bouallouche-Medjkoune, L., Aissani, D., Mani, A., & Ariouat, H. (2018). Clustering with load balancing-based routing protocol for wireless sensor networks. Wireless Personal Communications,103, 1–21.

    Google Scholar 

  23. Tall, H., Chalhoub, G., Hakem, N., & Misson, M. (2017). Load balancing routing with queue overflow prediction for wireless sensor networks. Wireless Networks,25, 1–11.

    Google Scholar 

  24. Luo, C., Chen, W., Yu, J., Wang, Y., & Li, D. (2018). A novel centralized algorithm for constructing virtual backbones in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking,55, 1–12.

    Google Scholar 

  25. Ragavan, P. S., & Ramasamy, K. (2018). Optimized routing in wireless sensor networks by establishing dynamic topologies based on genetic algorithm. Cluster Computing,22, 1–7.

    Google Scholar 

  26. Soni, V., & Mallick, D. K. (2018). Fuzzy logic based multihop topology control routing protocol in wireless sensor networks. Microsystem Technologies,24(5), 2357–2369.

    Google Scholar 

  27. Mittal, N., Singh, U., & Sohi, B. S. (2018). An energy-aware cluster-based stable protocol for wireless sensor networks. Neural Computing and Applications,31, 1–18.

    Google Scholar 

  28. Dener, M. (2018). A new energy efficient hierarchical routing protocol for wireless sensor networks. Wireless Personal Communications,101(1), 269–286.

    Google Scholar 

  29. Akila, I. S., & Venkatesan, R. (2018). An energy balanced geo-cluster head set based multi-hop routing for wireless sensor networks. Cluster Computing,22, 1–10.

    Google Scholar 

  30. AlFarraj, O., AlZubi, A., & Tolba, A. (2018). Trust-based neighbor selection using activation function for secure routing in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing 1–11.

  31. Hamidzadeh, J., & Ghomanjani, M. H. (2018). An unequal cluster-radius approach based on node density in clustering for wireless sensor networks. Wireless Personal Communications,101(3), 1619–1637.

    Google Scholar 

  32. Qin, D., Ji, P., Yang, S., & Berhane, T. M. (2018). An efficient data collection and load balance algorithm in wireless sensor networks. Wireless Networks,25, 1–12.

    Google Scholar 

  33. Wang, S., Yu, J., Atiquzzaman, M., Chen, H., & Ni, L. (2018). CRPD: A novel clustering routing protocol for dynamic wireless sensor networks. Personal and Ubiquitous Computing,22(3), 545–559.

    Google Scholar 

  34. Li, L., & Li, D. (2018). An energy-balanced routing protocol for a wireless sensor network. Journal of Sensors,2018, 1–13.

    Google Scholar 

  35. Dhand, G., & Tyagi, S. S. (2019). SMEER: Secure multi-tier energy-efficient routing protocol for hierarchical wireless sensor networks. Wireless Personal Communications,105(1), 17–35.

    Google Scholar 

  36. Selvi, M., Thangaramya, K., Ganapathy, S., Kulothungan, K., Nehemiah, H., & Kannan, A. (2019). An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wireless Personal Communications,105(4), 1475–1490.

    Google Scholar 

  37. Kalidoss, T., Rajasekaran, L., Kanagasabai, K., Sannasi, G., & Kannan, A. (2019). QoS aware trust-based routing algorithm for wireless sensor networks. Wireless Personal Communications 1–22.

  38. Zakariayi, S., & Babaie, S. (2018). DEHCIC: A distributed energy-aware hexagon based clustering algorithm to improve coverage in wireless sensor networks. Peer-to-Peer Networking and Applications,12, 1–16.

    Google Scholar 

  39. Lai, W. K., & Fan, C.-S. (2017). Novel node deployment strategies in corona structure for wireless sensor networks. IEEE Access,5, 3889–3899.

    Google Scholar 

  40. Chatterjee, P., Ghosh, S. C., & Das, N. (2017). Load balanced coverage with graded node deployment in wireless sensor networks. IEEE Transactions on Multi-Scale Computing Systems,3(2), 100–112.

    Google Scholar 

  41. Rahman, A. U., Alharby, A., Hasbullah, H., & Almuzaini, K. (2016). Corona based deployment strategies in wireless sensor network: a survey. Journal of Network and Computer Applications,64, 176–193.

    Google Scholar 

  42. Mazinani, A., Mazinami, S. M., & Mirzaie, M. (2018). FMCR-CT: An energy-efficient fuzzy multi-cluster—based routing with a constant threashold in wireless sensor network. Alexandria Engineering Journal,58, 127–141.

    Google Scholar 

  43. Robinson, Y. H., Julie, E. G., Balaji, S., & Ayyasamy, A. (2016). Energy-aware clustering scheme in wireless sensor network using neuro-fuzzy approach. Wireless Personal Communications,95, 703–721.

    Google Scholar 

  44. Rewadkar, D., & Doye, D. (2018). Multi-objective auto-regressive whale optimization for traffic-aware routing in urban VANET. IET Information Security,12, 1–12.

    Google Scholar 

  45. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software,95, 51–67.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, PESCE, Mandya, India

    M. Revanesh & V. Sridhar

  2. Department of ECE, Portland State University, Portland, USA

    John M. Acken

Authors
  1. M. Revanesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Sridhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John M. Acken
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Revanesh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Revanesh, M., Sridhar, V. & Acken, J.M. Secure Coronas Based Zone Clustering and Routing Model for Distributed Wireless Sensor Networks. Wireless Pers Commun 112, 1829–1857 (2020). https://doi.org/10.1007/s11277-020-07129-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07129-0

Keywords

Search

Navigation